Skip to content

Version 0.09 (Alpha) - 21/02/2025

Pre-release
Pre-release
Compare
Choose a tag to compare
@DavidOsipov DavidOsipov released this 21 Feb 09:35
· 21 commits to main since this release
0.09
263d089
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

This release builds on the foundation laid in 0.05 and 0.051, introducing significant enhancements to keyword extraction, semantic analysis, and overall robustness of the ATS Optimizer. While progress has been made on precision and functionality, some known issues remain unresolved, and new challenges have emerged.

Major Changes

Enhanced Keyword Extraction

  • Entity Ruler Integration: Added whitelisted phrases from skills_whitelist as SKILL entities in the spaCy pipeline, preserving multi-word skills (e.g., "machine learning") during tokenization.
  • Improved N-Gram Generation: Updated _generate_ngrams to filter out single-letter tokens and stop words, ensuring cleaner and more relevant keyword sets for TF-IDF analysis.
  • Refined Keyword Extraction: Enhanced extract_keywords to combine preserved SKILL entities with tokenized keywords, improving accuracy for technical terms.

Semantic Analysis Improvements

  • Enabled by Default: Set semantic_validation: True in the config to filter keywords based on context, reducing irrelevant terms.
  • Stricter Similarity Threshold: Increased similarity_threshold from 0.6 to 0.65 for more precise semantic categorization.
  • N-Gram Range Adjustment: Reduced ngram_range and whitelist_ngram_range from [1, 3] to [1, 2] to focus on shorter, actionable phrases.

Robustness and Debugging

  • TF-IDF Matrix Validation: Improved _create_tfidf_matrix with pre-vectorization filtering of invalid tokens (e.g., single-letter words) and added debug logging for better traceability.
  • SpaCy Pipeline: Added sentencizer to the model loading process for consistent sentence boundary detection.
  • Code Cleanup: Removed unused Pool import from multiprocessing and streamlined imports for better maintainability.

New Features

  • Preservation of Whitelisted Skills: Multi-word skills from skills_whitelist are now recognized as entities, ensuring they remain intact in the output (e.g., "product management" instead of split terms).
  • Dynamic N-Gram Filtering: Automatically excludes noisy n-grams containing stop words or single characters.

Resolved Issues

  • None from previous versions fully resolved yet (see Known Issues).

Known Issues

  • [Critical, Unresolved from 0.05] Incorrectly Displayed Keywords in Excel: The Summary sheet shows single-word keywords (e.g., "science", "cross") with suspiciously uniform scores (e.g., 1.42192903 for multiple terms), indicating a potential scoring or aggregation issue. Multi-word phrases from skills_whitelist are not consistently appearing as expected.
  • [Critical, Unresolved from 0.05] Unreliable Unit Tests: The test suite remains untested and fails consistently, lacking coverage for critical components like keyword preservation and scoring.
  • [New, High Priority] Inconsistent Whitelist Application: The Detailed Scores sheet shows many keywords marked as In Whitelist: FALSE despite being in skills_whitelist (e.g., "product owner"), suggesting an issue with whitelist matching or entity recognition.
  • [New, Medium Priority] Low TF-IDF Variance: TF-IDF scores in the Detailed Scores sheet are often identical (e.g., 0.049574662), indicating potential issues with document differentiation or scoring normalization.

Sample Output Analysis

  • Summary Page: Keywords like "science", "cross", and "technical" have identical Total_Score (1.42192903) and Avg_Score (0.473976343) across 3 jobs, suggesting a possible bug in score calculation or keyword weighting.
  • Detailed Scores: Multi-word phrases (e.g., "product owner", "leveraging llms") appear, but their In Whitelist status is inconsistent, and scores are low (0.034702263), possibly due to TF-IDF dilution or whitelist boost not applying correctly.

Dependencies

  • nltk
  • numpy
  • pandas
  • spacy
  • scikit-learn
  • pyyaml
  • psutil
  • hashlib

Future Improvements

  • [High Priority] Fix Keyword Display and Scoring: Address the uniform scoring and missing multi-word keywords in the Excel output.
  • [High Priority] Overhaul Unit Tests: Develop comprehensive tests to validate entity recognition, whitelist application, and scoring accuracy.
  • [Medium Priority] Enhance Whitelist Boost: Ensure whitelist_boost (1.5) is consistently applied to whitelisted terms.
  • [Medium Priority] Optimize TF-IDF: Investigate low variance in TF-IDF scores and improve differentiation across documents.
  • Further refine semantic filtering for domain-specific terms.
  • Enhance logging to pinpoint scoring and categorization issues.

What's Changed

Full Changelog: 0.051...0.09

Contributors

renovate and DavidOsipov
Assets 4
Loading